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This paper introduces the Special Issue 'Hydrological Model Intercomparison for Climate Impact Assessment'. We describe the river basins used as case studies, the input data, the hydrological models, and the climate scenarios applied in the multi-model framework. We also summarize the main results of the papers contained in this Special Issue. [ABSTRACT FROM AUTHOR]

In this paper, we assess the quality of state-of-the-art regional climate information intended to support climate adaptation decision-making. We use the UK Climate Projections 2018 as an example of such information. Their probabilistic, global, and regional land projections exemplify some of the key methodologies that are at the forefront of constructing regional climate information for decision support in adapting to a changing climate. We assess the quality of the evidence and the methodology used to support their statements about future regional climate along six quality dimensions: transparency; theory; independence, number, and comprehensiveness of evidence; and historical empirical adequacy. The assessment produced two major insights. First, a major issue that taints the quality of UKCP18 is the lack of transparency, which is particularly problematic since the information is directed towards non-expert users who would need to develop technical skills to evaluate the quality and epistemic reliability of this information. Second, the probabilistic projections are of lower quality than the global projections because the former lack both transparency and a theory underpinning the method used to produce quantified uncertainty estimates about future climate. The assessment also shows how different dimensions are satisfied depending on the evidence used, the methodology chosen to analyze the evidence, and the type of statements that are constructed in the different strands of UKCP18. This research highlights the importance of knowledge quality assessment of regional climate information that intends to support climate change adaptation decisions. [ABSTRACT FROM AUTHOR]

This paper presents an assessment of the implications of climate change for global river flood risk. It is based on the estimation of flood frequency relationships at a grid resolution of 0.5 × 0.5°, using a global hydrological model with climate scenarios derived from 21 climate models, together with projections of future population. Four indicators of the flood hazard are calculated; change in the magnitude and return period of flood peaks, flood-prone population and cropland exposed to substantial change in flood frequency, and a generalised measure of regional flood risk based on combining frequency curves with generic flood damage functions. Under one climate model, emissions and socioeconomic scenario (HadCM3 and SRES A1b), in 2050 the current 100-year flood would occur at least twice as frequently across 40 % of the globe, approximately 450 million flood-prone people and 430 thousand km of flood-prone cropland would be exposed to a doubling of flood frequency, and global flood risk would increase by approximately 187 % over the risk in 2050 in the absence of climate change. There is strong regional variability (most adverse impacts would be in Asia), and considerable variability between climate models. In 2050, the range in increased exposure across 21 climate models under SRES A1b is 31-450 million people and 59 to 430 thousand km of cropland, and the change in risk varies between −9 and +376 %. The paper presents impacts by region, and also presents relationships between change in global mean surface temperature and impacts on the global flood hazard. There are a number of caveats with the analysis; it is based on one global hydrological model only, the climate scenarios are constructed using pattern-scaling, and the precise impacts are sensitive to some of the assumptions in the definition and application. [ABSTRACT FROM AUTHOR]

The overall global-scale consequences of climate change are dependent on the distribution of impacts across regions, and there are multiple dimensions to these impacts. This paper presents a global assessment of the potential impacts of climate change across several sectors, using a harmonised set of impacts models forced by the same climate and socio-economic scenarios. Indicators of impact cover the water resources, river and coastal flooding, agriculture, natural environment and built environment sectors. Impacts are assessed under four SRES socio-economic and emissions scenarios, and the effects of uncertainty in the projected pattern of climate change are incorporated by constructing climate scenarios from 21 global climate models. There is considerable uncertainty in projected regional impacts across the climate model scenarios, and coherent assessments of impacts across sectors and regions therefore must be based on each model pattern separately; using ensemble means, for example, reduces variability between sectors and indicators. An example narrative assessment is presented in the paper. Under this narrative approximately 1 billion people would be exposed to increased water resources stress, around 450 million people exposed to increased river flooding, and 1.3 million extra people would be flooded in coastal floods each year. Crop productivity would fall in most regions, and residential energy demands would be reduced in most regions because reduced heating demands would offset higher cooling demands. Most of the global impacts on water stress and flooding would be in Asia, but the proportional impacts in the Middle East North Africa region would be larger. By 2050 there are emerging differences in impact between different emissions and socio-economic scenarios even though the changes in temperature and sea level are similar, and these differences are greater in 2080. However, for all the indicators, the range in projected impacts between different climate models is considerably greater than the range between emissions and socio-economic scenarios. [ABSTRACT FROM AUTHOR]

Conventional forecast driven approaches to climate change adaptation create a cascade of uncertainties that can overwhelm decision makers and delay proactive adaptation responses. Robust Decision Making inverts the analytical steps associated with forecast-led methodologies, reframing adaptation in the context of a specific decision maker's capacities and vulnerabilities. In adopting this bottom-up approach, the aim is to determine adaptation solutions which are insensitive to uncertainty. Yet despite the increased use of the approach in large-scale adaptation projects in developed countries, there is little empirical evidence to test whether or not it can be successfully applied in developing countries. The complex realities of decision making processes, the need to combine quantitative data with qualitative understanding and competing environmental, socio-economic and political factors all pose significant obstacles to adaptation. In developing countries, these considerations are particularly relevant and additional pressures exist which may limit the uptake and utility of the Robust Decision Making approach. In this paper, we investigate the claim that the approach can be deemed valuable in developing countries. Challenges and opportunities associated with Robust Decision Making, as a heuristic decision framework, are discussed with insights from a case study of adapting coastal infrastructure to changing environmental risks in South Africa. Lessons are extracted about the ability of this framework to improve the handling of uncertainty in adaptation decisions in developing countries. [ABSTRACT FROM AUTHOR]

Motivated by growing heat-related morbidity and mortality in a warming climate, this paper assesses global heat health risk in order to understand the challenges to sustainability in the 21st century, using four Representative Concentration Pathways (RCPs) of the HadGEM2-ES climate model and five Shared Socio-Economic Pathways (SSPs). Factors influencing global heat health risk were reviewed and risks were estimated based on heat hazard and socio-economic vulnerability. Hazard, vulnerability, risk and in particular, populations at different risk levels, were analyzed quantitatively at both global and regional scales. The results show that under an RCP8.5-SSP3 scenario, the world will be subject to the highest heat health risk, with rapidly increasing hazard levels and vulnerability over the century. Less developed or developing regions, such as Africa and Southeast Asia, are at the highest risk. The heat risk under an RCP2.6-SSP1 scenario will first increase and then fall, resulting in the lowest heat-health-risk pattern. We found that heat health risk will increase during the century under all RCP-SSP scenarios, with a higher frequency, higher intensity, longer duration and expanding spatial reach. Significant differences were observed across regions. The results make clear that the increasing risk poses significant challenges to sustainable human health. To meet these challenges, more attention and effective actions are urgently needed from both policy makers and individuals. [ABSTRACT FROM AUTHOR]

The paper aims to explore the biasness in the RegCM climate model outputs for diverse agro-climatic zones of Uttar Pradesh, India, with emphasis on wheat (Rabi growing season) and rice (Kharif growing season) yields with and without bias correction using quantile mapping approach for the baseline period of 1971-2000. The result shows that RCM highly underestimated the maximum and minimum temperature. There exists a bias towards lower precipitation in annual and Kharif and higher in Rabi along with strikingly low intense warm (maximum temperature > 45 °C and 40 °C) and high cold events (maximum temperature < 20 °C and minimum temperature < 5 °C) in the RCM simulation and biased towards low extreme rainfall > 50 mm/day. Bias correction through quantile mapping approach, however, showed excellent agreement for annual and seasonal maximum and minimum temperature and satisfactory for extreme temperatures but drastically failed to correct rainfall. The study also quantified the biasness in the simulated potential, irrigated, and rainfed wheat and rice yield using DSSAT (Decision Support System for Agro-technology Transfer) crop model by employing observed, RCM baseline, and RCM baseline bias-corrected weather data. The grain yields of RCM-simulated wheat and rice were high while the bias-corrected yield has shown good agreement with corresponding observed yield. Future research must account for the development of more reliable RCM models and explicitly bias correction method in specific to complement future analysis. [ABSTRACT FROM AUTHOR]

Both statistical and dynamical downscaling methods are well established techniques to bridge the gap between the coarse information produced by global circulation models and the regional-to-local scales required by the climate change Impacts, Adaptation, and Vulnerability (IAV) communities. A number of studies have analyzed the relative merits of each technique by inter-comparing their performance in reproducing the observed climate, as given by a number of climatic indices (e.g. mean values, percentiles, spells). However, in this paper we stress that fair comparisons should be based on indices that are not affected by the calibration towards the observed climate used for some of the methods. We focus on precipitation (over continental Spain) and consider the output of eight Regional Climate Models (RCMs) from the EURO-CORDEX initiative at 0.44 resolution and five Statistical Downscaling Methods (SDMs) -analog resampling, weather typing and generalized linear models- trained using the Spain044 observational gridded dataset on exactly the same RCM grid. The performance of these models is inter-compared in terms of several standard indices -mean precipitation, 90th percentile on wet days, maximum precipitation amount and maximum number of consecutive dry days- taking into account the parameters involved in the SDM training phase. It is shown, that not only the directly affected indices should be carefully analyzed, but also those indirectly influenced (e.g. percentile-based indices for precipitation) which are more difficult to identify. We also analyze how simple transformations (e.g. linear scaling) could be applied to the outputs of the uncalibrated methods in order to put SDMs and RCMs on equal footing, and thus perform a fairer comparison. [ABSTRACT FROM AUTHOR]

The parameter uncertainty of process-based models has received little attention in climate change impact studies. This paper aims to integrate parameter uncertainty into simulations of climate change impacts on forest net primary productivity (NPP). We used either prior (uncalibrated) or posterior (calibrated using Bayesian calibration) parameter variations to express parameter uncertainty, and we assessed the effect of parameter uncertainty on projections of the process-based model 4C in Scots pine ( Pinus sylvestris) stands under climate change. We compared the uncertainty induced by differences between climate models with the uncertainty induced by parameter variability and climate models together. The results show that the uncertainty of simulated changes in NPP induced by climate model and parameter uncertainty is substantially higher than the uncertainty of NPP changes induced by climate model uncertainty alone. That said, the direction of NPP change is mostly consistent between the simulations using the standard parameter setting of 4C and the majority of the simulations including parameter uncertainty. Climate change impact studies that do not consider parameter uncertainty may therefore be appropriate for projecting the direction of change, but not for quantifying the exact degree of change, especially if parameter combinations are selected that are particularly climate sensitive. We conclude that if a key objective in climate change impact research is to quantify uncertainty, parameter uncertainty as a major factor driving the degree of uncertainty of projections should be included. [ABSTRACT FROM AUTHOR]

This paper presents a global scale assessment of the impact of climate change on water scarcity. Patterns of climate change from 21 Global Climate Models (GCMs) under four SRES scenarios are applied to a global hydrological model to estimate water resources across 1339 watersheds. The Water Crowding Index (WCI) and the Water Stress Index (WSI) are used to calculate exposure to increases and decreases in global water scarcity due to climate change. 1.6 (WCI) and 2.4 (WSI) billion people are estimated to be currently living within watersheds exposed to water scarcity. Using the WCI, by 2050 under the A1B scenario, 0.5 to 3.1 billion people are exposed to an increase in water scarcity due to climate change (range across 21 GCMs). This represents a higher upper-estimate than previous assessments because scenarios are constructed from a wider range of GCMs. A substantial proportion of the uncertainty in the global-scale effect of climate change on water scarcity is due to uncertainty in the estimates for South Asia and East Asia. Sensitivity to the WCI and WSI thresholds that define water scarcity can be comparable to the sensitivity to climate change pattern. More of the world will see an increase in exposure to water scarcity than a decrease due to climate change but this is not consistent across all climate change patterns. Additionally, investigation of the effects of a set of prescribed global mean temperature change scenarios show rapid increases in water scarcity due to climate change across many regions of the globe, up to 2 °C, followed by stabilisation to 4 °C. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses reports within the issue on topics including the pattern-scaling approach used to construct climate scenarios, the global-scale impacts models developed in the QUEST-GSI project, and the impact to change in global mean surface temperature.

In Integrated Assessment modelling of climate change impacts and adaptation, there are two main uses for measures of capacity to adapt to climate change. The first is to represent the capacity for proactive adaptation: this can be termed adaptive capacity. The second is to represent the capacity for reactive or instantaneous coping: this can be termed coping capacity. Adaptive capacity helps to determine which proactive adaptation options are feasible as inputs to the models under any given pair of climate and socio-economic scenarios. Coping capacity represents the residual ability to react to conditions, and influences vulnerability under any given set of model outputs. Using the example of the CLIMSAVE Integrated Assessment Platform, we explain how these capacities can be represented in integrated assessment. We demonstrate how an index of adaptive and coping capacity can be developed using a five-capitals (human, social, manufactured, natural, financial) model of societal wealth and incorporated in integrated assessment models. We find that for very aggregate applications, but not local or sectoral applications, the same indicators can be used to simulate adaptive and coping capacity. In addition, we argue that it is generally unnecessary to account for the depletion of capacity through adaptation itself, and that natural capital can generally be omitted from capacity measures if it is already directly represented in model outputs. [ABSTRACT FROM AUTHOR]

Using an ensemble of 10 statistically downscaled global climate model (GCM) simulations, we project future climate change impacts on the state of Indiana (IN) for two scenarios of greenhouse gas concentrations (a medium scenario—RCP4.5 and a high scenario—RCP 8.5) for three future time periods (2020s, 2050s, 2080s). Relative to a 1971–2000 baseline, the projections show substantial changes in temperature (T) for IN, with a change in the annual ensemble mean T for the 2080s RCP8.5 scenario of about 5.6 °C (10.1 °F). Such changes also indicate major changes in T extremes. For southern IN, the number of days with daily maximum T above 35 °C (95 °F) is projected to be about 100 days per year for the 2080s RCP8.5 scenario, as opposed to an average of 5 days for the historical baseline climate. Locations in northern IN could experience 50 days per year above 35 °C (95 °F) for the same conditions. Energy demand for cooling, as measured by cooling degree days (CDD), is projected to increase nearly fourfold in response to this extreme warming, but heating demand as measured by heating degree days (HDD) is projected to decline by 30%, which would result in a net reduction in annual heating/cooling energy demand for consumers. The length of the growing season is projected to increase by about 30 to 50 days by the 2080s for the RCP8.5 scenario, and USDA hardiness zones are projected to shift by about one full zone throughout IN. By the 2080s, all GCM simulations for the RCP8.5 scenario show higher annual precipitation (P) over the Midwest and IN. Projected seasonal changes in P include a 25–30% increase in winter and spring by the 2080s for the RCP8.5 scenarios and a 1–7% decline in summer and fall P (although there is a low model agreement in the latter two seasons). Rising T is projected to cause systematic decreases in the snow-to-rain ratio from Nov-Mar. Snow is projected to become uncommon in southern IN by the 2080s for the RCP8.5 scenario, and snowfall is substantially reduced in other areas of the state. The combined effects of these changes in T, P, and snowfall will likely result in increased surface runoff and flooding during winter and spring. [ABSTRACT FROM AUTHOR]

Climate change has substantial impacts on regional hydrology in the major river basins. To figure out such latent hydrological impacts of changing climate, more reliable hydrological simulations are imperative. In this study, we evaluated the impacts of climate change on hydrological regime in the Upper Yangtze River Basin based on four downscaled and bias-corrected Global Climate Model outputs from Coupled Model Intercomparison Project Phase 5 under four Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) driving three hydrological models. Two model evaluation approaches were applied: simple and comprehensive. The comprehensive approach was used to evaluate models in the historical period, optimizing objective function at four gauges, and hydrological models were weighted for impact assessment based on their performance. In such a way, projected streamflow time series are obtained under different emission scenarios. Results show that the annual average discharge is projected to increase by 4.1–10.5% under the RCP scenarios at the end of twenty-first century relative to the reference period (1970–1999). Moreover, the high flow is projected to increase and the low flow to decrease indicating a higher probability of flood and drought occurrence in the basin. The severity of floods and droughts may increase. In comparison with the simple one-site model evaluation approach, the comprehensive method reveals that the anticipated extreme flow events would be less severe, and annual mean discharge slightly lower. The projected results imply that application of the comprehensive model evaluation approach could narrow the simulated spreads of projections significantly, and might provide more credible results. [ABSTRACT FROM AUTHOR]

This study performs a comparison of two model calibration/validation approaches and their influence on future hydrological projections under climate change by employing two climate scenarios (RCP2.6 and 8.5) projected by four global climate models. Two hydrological models (HMs), snowmelt runoff model + glaciers and variable infiltration capacity model coupled with a glacier model, were used to simulate streamflow in the highly snow and glacier melt–driven Upper Indus Basin. In the first (conventional) calibration approach, the models were calibrated only at the basin outlet, while in the second (enhanced) approach intermediate gauges, different climate conditions and glacier mass balance were considered. Using the conventional and enhanced calibration approaches, the monthly Nash-Sutcliffe Efficiency (NSE) for both HMs ranged from 0.71 to 0.93 and 0.79 to 0.90 in the calibration, while 0.57–0.92 and 0.54–0.83 in the validation periods, respectively. For the future impact assessment, comparison of differences based on the two calibration/validation methods at the annual scale (i.e. 2011–2099) shows small to moderate differences of up to 10%, whereas differences at the monthly scale reached up to 19% in the cold months (i.e. October–March) for the far future period. Comparison of sources of uncertainty using analysis of variance showed that the contribution of HM parameter uncertainty to the overall uncertainty is becoming very small by the end of the century using the enhanced approach. This indicates that enhanced approach could potentially help to reduce uncertainties in the hydrological projections when compared to the conventional calibration approach. [ABSTRACT FROM AUTHOR]

The effect of model calibration on the projection of climate change impact on hydrological indicators was assessed by employing variants of a pan-European hydrological model driven by forcing data from an ensemble of climate models. The hydrological model was calibrated using three approaches: calibration at the outlets of major river basins, regionalization through calibration of smaller scale catchments with unique catchment characteristics, and building a model ensemble by sampling model parameters from the regionalized model. The large-scale patterns of the change signals projected by all model variants were found to be similar for the different indicators. Catchment scale differences were observed between the projections of the model calibrated for the major river basins and the other two model variants. The distributions of the median change signals projected by the ensemble model were found to be similar to the distributions of the change signals projected by the regionalized model for all hydrological indicators. The study highlights that the spatial detail to which model calibration is performed can highly influence the catchment scale detail in the projection of climate change impact on hydrological indicators, with an absolute difference in the projections of the locally calibrated model and the model calibrated for the major river basins ranging between 0 and 55% for mean annual discharge, while it has little effect on the large-scale pattern of the projection. [ABSTRACT FROM AUTHOR]

The assessment of climate change and its impact relies on the ensemble of models available and/or sub-selected. However, an assessment of the validity of simulated climate change impacts is not straightforward because historical data is commonly used for bias-adjustment, to select ensemble members or to define a baseline against which impacts are compared—and, naturally, there are no observations to evaluate future projections. We hypothesize that historical streamflow observations contain valuable information to investigate practices for the selection of model ensembles. The Danube River at Vienna is used as a case study, with EURO-CORDEX climate simulations driving the COSERO hydrological model. For each selection method, we compare observed to simulated streamflow shift from the reference period (1960–1989) to the evaluation period (1990–2014). Comparison against no selection shows that an informed selection of ensemble members improves the quantification of climate change impacts. However, the selection method matters, with model selection based on hindcasted climate or streamflow alone is misleading, while methods that maintain the diversity and information content of the full ensemble are favorable. Prior to carrying out climate impact assessments, we propose splitting the long-term historical data and using it to test climate model performance, sub-selection methods, and their agreement in reproducing the indicator of interest, which further provide the expectable benchmark of near- and far-future impact assessments. This test is well-suited to be applied in multi-basin experiments to obtain better understanding of uncertainty propagation and more universal recommendations regarding uncertainty reduction in hydrological impact studies. [ABSTRACT FROM AUTHOR]

Current-climate precipitation and temperature extremes have been identified by decision makers in West Africa as among the more impactful weather events causing lasting socioeconomic damage. In this article, we use a plausible future-climate scenario (RCP8.5) for the end of the twenty-first century to explore the relative commonness of such extremes under global warming. The analysis presented considers what a typical day in the future climate will feel like relative to current extrema. Across much of West Africa, we see that the typical future-climate day has maximum and minimum temperatures greater than 99.5% of currently experienced values. This finding exists for most months but is particularly pronounced during the Boreal spring and summer. The typical future precipitation event has a daily rainfall rate greater than 95% of current storms. These findings exist in both a future scenario model run with and without parameterised convection, and for many of the Coupled Model Inter-comparison Project version 5 ensemble members. Additionally, agronomic monsoon onset is projected to occur later and have greater inter-annual variability in the future. Our findings suggest far more extreme conditions in future climate over West Africa. The projected changes in temperature and precipitation could have serious socioeconomic implications, stressing the need for effective mitigation given the potential lack of adaptation pathways available to decision makers. [ABSTRACT FROM AUTHOR]

The study investigates the future climate change in the Jammu and Kashmir (J&K) Himalaya, India, by the end of the twenty-first century under 3 emission scenarios and highlights the changes in the distribution of the prevalent climate zones in the region. The multi-model climate high-resolution projections for the baseline period (1961–1990) are validated against the observed climate variables from 8 meteorological stations in the region. The temperature projections from the GFDL CM2.1 model are found in good agreement with the observations; however, no single model investigated in the present study reasonably simulates precipitation and therefore multi-model ensemble is used for precipitation projections. The average annual temperature is projected to increase by 4.5 °C, 3.98 °C, and 6.93 °C by the end of the twenty-first century under A1B, RCP4.5, and RCP8.5 scenarios, respectively. In contrast, an insignificant variation in precipitation projection is observed under all the 3 scenarios. The analysis indicates that, unlike the 13 climate zones under the updated Köppen-Geiger climate classification scheme, the J&K Himalaya broadly falls into 10 main climate zones only namely, "3 subtropical (~ 11%), 4 temperate (~ 19%), and 3 cold desert (~ 70%) zones". The projected climate change under the 3 emission scenarios indicates significant changes in the distribution of prevalent climate zones. The cold desert climate zone in the Ladakh region would shrink by ~ 22% and correspondingly the subtropical and temperate zones would expand due to the projected climate change. This information is vital for framing robust policies for adaptation and mitigation of the climate change impacts on various socio-economic and ecological sectors in the region. [ABSTRACT FROM AUTHOR]

The climate sensitivity of global climate models (GCMs) strongly influences projected climate change due to increased atmospheric carbon dioxide. Reasonably, the climate sensitivity of a GCM may be expected to affect dynamically downscaled projections. However, there has been little examination of the effect of the climate sensitivity of GCMs on regional climate model (RCM) ensembles. Therefore, we present projections of temperature and precipitation from the ensemble of projections produced as a part of the North American branch of the international Coordinated Regional Downscaling Experiment (NA-CORDEX) in the context of their relationship to the climate sensitivity of their parent GCMs. NA-CORDEX simulations were produced at 50-km and 25-km resolutions with multiple RCMs which downscaled multiple GCMs that spanned nearly the full range of climate sensitivity available in the CMIP5 archive. We show that climate sensitivity is a very important source of spread in the NA-CORDEX ensemble, particularly for temperature. Temperature projections correlate with driving GCM climate sensitivity annually and seasonally across North America not only at a continental scale but also at a local-to-regional scale. Importantly, the spread in temperature projections would be reduced if only low, mid, or high climate sensitivity simulations were considered, or if only the ensemble mean were considered. Precipitation projections correlate with climate sensitivity, but only at a continental scale during the cold season, due to the increasing influence of other processes at finer scales. Additionally, it is shown that the RCMs do alter the projection space sampled by their driving GCMs. [ABSTRACT FROM AUTHOR]

Bias correction methods have now emerged as the most commonly used approach when applying climate model outputs to impact studies. However, comparatively much fewer studies have looked at the limitations of bias correction caused by the very nature of the climate system. Two main sources of errors can affect the efficiency of bias correction over a future period: climate sensitivity and internal variability of the climate system. The former is related to differences in the forcing response between a climate model and the real climate system, whereas the latter results from the chaotic nature of the climate system. Using a "pseudo-reality" approach, this study investigates the contribution of these two sources of error to remaining biases of climate model after bias correction for future periods. The pseudo-reality approach uses one climate model as a reference dataset to correct other climate models. Results indicate that bias correction is beneficial over the reference period and in near future periods. However, large biases remain in future periods. The difference in climate sensitivities is the main contributor to the remaining biases in corrected data. Internal variability affects the near and far future similarly and may dominate in the near future, especially for precipitation. The impact of differences in climate sensitivity between the reference dataset and climate model data cannot be eliminated, while the impact of internal variability can be lessened by using a reference period for as long as possible to filter out low-frequency modes of variability. [ABSTRACT FROM AUTHOR]

We investigate climate change impacts transferred via foreign trade to Germany, a country that is heavily engaged in international trade. Specifically, we look at temperature changes and the associated labour productivity losses at a global scale until 2050. We assess the effects on Germany's imports and exports by means of a global computable general equilibrium (CGE) model. To address uncertainty, we account for three Shared Socioeconomic Pathways (SSP1, SSP2 and SSP3) and two Representative Concentration Pathways (RCP4.5 and RCP8.5) using projections from five global climate models. We find that average annual labour productivity for high intensity work declines by up to 31% for RCP4.5 (and up to 38% for RCP8.5) in Southeast Asia and the Middle East by 2050, all relative to a 2050 baseline without climate change. As a consequence, for RCP8.5, Germany's imports from regions outside Europe are lower by up to 2.46%, while imports from within Europe partly compensate this reduction. Also, Germany's exports to regions outside Europe are lower, but total exports increase by up to 0.16% due to higher exports to EU regions. Germany's GDP and welfare, however, are negatively affected with a loss of up to − 0.41% and − 0.46%, respectively. The results highlight that overall positive trade effects for Germany constitute a comparative improvement rather than an absolute gain with climate change. [ABSTRACT FROM AUTHOR]

South America contributes to roughly 30% of global runoff to the oceans. Because the regional economy and biodiversity depend significantly on its water resources, assessing potential climate change impacts on the continental water balance is crucial to support water management planning. Here we evaluate the mean alterations of water balance variables and river discharge in South America by the end of this century using two different GHG scenarios (RCP4.5 and RCP8.5). An ensemble comprising 25 global climate models (GCM) from CMIP5 is used to force a continental-scale hydrologic-hydrodynamic model developed for that region. A negative signal with respect to changes in precipitation, evapotranspiration, and runoff is observed on most of the continent. Major decreases in the annual mean discharge are expected for the Orinoco, Tocantins, and Amazon basins, which would be around 8–14% at least (statistically significant – RCP4.5 and RCP8.5, respectively). Only the Uruguay Basin presents a positive trend for the mean discharge. [ABSTRACT FROM AUTHOR]

Intensity-duration-frequency (IDF) curves, commonly used in stormwater infrastructure design to represent characteristics of extreme rainfall, are gradually being updated to reflect expected changes in rainfall under climate change. The modeling choices used for updating lead to large uncertainties; however, it is unclear how much these uncertainties affect the design and cost of stormwater systems. This study investigates how the choice of spatial resolution of the regional climate model (RCM) ensemble and the spatial adjustment technique affect climate-corrected IDF curves and resulting stormwater infrastructure designs in 34 US cities for the period 2020 to 2099. In most cities, IDF values are significantly different between three spatial adjustment techniques and two RCM spatial resolutions. These differences have the potential to alter the size of stormwater systems designed using these choices and affect the results of climate impact modeling more broadly. The largest change in the engineering decision results when the design storm is selected from the upper bounds of the uncertainty distribution of the IDF curve, which changes the stormwater pipe design size by five increments in some cases, nearly doubling the cost. State and local agencies can help reduce some of this variability by setting guidelines, such as avoiding the use of the upper bound of the future uncertainty range as a design storm and instead accounting for uncertainty by tracking infrastructure performance over time and preparing for adaptation using a resilience plan. [ABSTRACT FROM AUTHOR]

Climate data obtained from global climate models (GCMs) form the basis of most studies of regional climate change and its impacts. Using the northeastern U.S. as a test case, we develop a framework to systematically sub-select reliable models for use in climate change studies in the region. Model performance over the historical period is evaluated first for a wide variety of standard and process metrics including large-scale atmospheric circulation features that drive regional climate variability. The inclusion of process-based metrics allows identification of credible models in capturing key processes relevant for the climate of the northeastern U.S. Model performance is then used in conjunction with the assessment of redundancy in model projections, especially in summer precipitation, to eliminate models that have better performing counterparts. Finally, we retain some mixed-performing models to maintain the range of climate model uncertainty, required by the fact that model biases are not strongly related to their respective projections. This framework leads to the retention of 16 of 36 CMIP5 GCMs that (a) have a satisfactory historical performance for a variety of metrics and (b) provide diverse climate projections consistent with uncertainties in the multi-model ensemble (MME). Overall, the models show significant variations in their performance across metrics and seasons with none emerging as the best model in all metrics. The retained set reduces the number of models by more than one half, easing the computational burden of using the entire CMIP5 MME, while still maintaining a wide range of projections for risk assessment. The retention of some mixed-performing models to maintain ensemble uncertainty suggests a potential to narrow the ranges in temperature and precipitation. But any further refinement should be based on a more detailed analysis of models in capturing regional climate variability and extremes to avoid providing overconfident projections. [ABSTRACT FROM AUTHOR]

We investigate the impact of climate change on the storm surges induced by extratropical cyclones (ETCs) between November and March. We quantify changes to the storm surge between a historical period (1979–2004) and a future period during the mid to late twenty-first century (2054–2079) for a number of major coastal cities in the Northeastern United States. Observed water levels are analyzed to estimate storm surges induced by ETCs during the historical period. A hydrodynamic model is utilized to simulate storm surges induced by ETCs projected for the future climate by seven global climate models. The biases in the hydrodynamic and climate models are calculated and removed from the simulated surge heights. Statistical methods, including the peaks-over-threshold method, are applied to estimate the storm surge return levels. We find that future projections based on most of the climate models indicate relatively small effects of climate change on ETC storm surges. The weighted-average projections over all climate models show a small increase in storm surge return levels (less than 7% increase in 10- and 50-year surge heights). However, uncertainties exist among the climate models and projections from one climate model show a substantial increase in the storm surge return levels (up to 27% and 36% increase in 10- and 50-year surge heights, respectively). These uncertainties, and likely the larger impact of sea level rise, should be accounted for in projecting the risk posted by ETC flooding. [ABSTRACT FROM AUTHOR]

Climate litigation has attracted renewed interest as a governance tool. A key challenge in climate litigation is to assess the factual basis of causation. Extreme weather attribution, specifically the Fraction of Attributable Risk (FAR), has been proposed as a way to tackle this challenge. What remains unclear is how attribution science interacts with the legal admissibility of evidence based on climate models. While evidence has to be legally admissible in order to be considered in a trial, it has to be reliable in order for the court to arrive at a legally correct conclusion. Since parties to the trial have incentives to produce evidence favorable to their case, admissibility requirements and the reliability of the evidence brought forward are linked. We provide a specific proposal for how to accommodate FAR estimates in admissibility standards by modifying an existing set of admissibility criteria, the Daubert criteria. We argue that two of the five Daubert criteria are unsuitable for dealing with such evidence and that replacing those criteria with ones directly addressing the reliability of FAR estimates is adequate. Lastly, we highlight the dependence of courts on both the existence and accessibility of a framework to determine the reliability of FAR estimates in executing such criteria. [ABSTRACT FROM AUTHOR]

Future climate projections and impact analyses are pivotal to evaluate the potential change in crop yield under climate change. Impact assessment of climate change is also essential to prepare and implement adaptation measures for farmers and policymakers. However, there are uncertainties associated with climate change impact assessment when combining crop models and climate models under different emission scenarios. This study quantifies the various sources of uncertainty associated with future climate change effects on wheat productivity at six representative sites covering dry and wet environments in Australia based on 12 soil types and 12 nitrogen application rates using one crop model driven by 28 global climate models (GCMs) under two representative concentration pathways (RCPs) at near future period 2021-2060 and far future period 2061-2100. We used the analysis of variance (ANOVA) to quantify the sources of uncertainty in wheat yield change. Our results indicated that GCM uncertainty largely dominated over RCPs, nitrogen rates, and soils for the projections of wheat yield at drier locations. However, at wetter sites, the largest share of uncertainty was nitrogen, followed by GCMs, soils, and RCPs. In addition, the soil types at two northern sites in the study area had greater effects on yield change uncertainty probably due to the interaction effect of seasonal rainfall and soil water storage capacity. We concluded that the relative contributions of different uncertainty sources are dependent on climatic location. Understanding the share of uncertainty in climate impact assessment is important for model choice and will provide a basis for producing more reliable impact assessment. [ABSTRACT FROM AUTHOR]

Future thermosteric and dynamic sea-level changes are often projected by process-based climate models. Emulation of such computationally expensive models helps enable model intercomparison over a range of forcing scenarios and thus enables additional analysis of sea-level rise projection uncertainty. Current emulation methods use linear response functions to estimate global mean sea-level response. Here, we introduce a novel dual model to emulate global mean thermosteric sea-level rise that incorporates short- and long-term responses to climate forcing. This nonlinear response function outperforms existing linear response functions over six illustrative general circulation models and the four representative concentration pathways. To emulate dynamic sea-level projections, we introduce a linear pattern scaling model that relates regional sea-level changes to global mean thermosteric sea-level rise. Pattern scaling is shown to reproduce strongly forced sea-level trends. Our results demonstrate effective emulation of global and regional sea-level rise, which can facilitate the consideration of sea-level rise projection uncertainty critical to the analysis of sea-level rise hazard. [ABSTRACT FROM AUTHOR]

This study evaluates regional-scale projections of climate indices that are relevant to climate change impacts in Canada. We consider indices of relevance to different sectors including those that describe heat conditions for different crop types, temperature threshold exceedances relevant for human beings and ecological ecosystems such as the number of days temperatures are above certain thresholds, utility relevant indices that indicate levels of energy demand for cooling or heating, and indices that represent precipitation conditions. Results are based on an ensemble of high-resolution statistically downscaled climate change projections from 24 global climate models (GCMs) under the RCP2.6, RCP4.5, and RCP8.5 emissions scenarios. The statistical downscaling approach includes a bias-correction procedure, resulting in more realistic indices than those computed from the original GCM data. We find that the level of projected changes in the indices scales well with the projected increase in the global mean temperature and is insensitive to the emission scenarios. At the global warming level about 2.1 °C above pre-industrial (corresponding to the multi-model ensemble mean for 2031-2050 under the RCP8.5 scenario), there is almost complete model agreement on the sign of projected changes in temperature indices for every region in Canada. This includes projected increases in extreme high temperatures and cooling demand, growing season length, and decrease in heating demand. Models project much larger changes in temperature indices at the higher 4.5 °C global warming level (corresponding to 2081-2100 under the RCP8.5 scenario). Models also project an increase in total precipitation, in the frequency and intensity of precipitation, and in extreme precipitation. Uncertainty is high in precipitation projections, with the result that models do not fully agree on the sign of changes in most regions even at the 4.5 °C global warming level. [ABSTRACT FROM AUTHOR]

Crop yields are vulnerable to climate change. We assess the global impacts of climate change on agricultural systems under two climate projections (RCP8.5 and RCP4.5) to quantify the difference in impacts if climate change were reduced. We also employ two different socioeconomic pathways (SSP3 and SSP5) to assess the sensitivity of results to the underlying socioeconomic conditions. The integrated-Population-Economy-Technology-Science (iPETS) model, a global integrated assessment model for projecting future energy use, land use and emissions, is used in conjunction with the Community Earth System Model (CESM), and particularly its land surface component, the Community Land Model (CLM), to evaluate climate change impacts on agriculture. iPETS results are produced at the level of nine world regions for the period 2005–2100. We employ climate impacts on crop yield derived from CLM, driven by CESM simulations of the two RCPs. These yield effects are applied within iPETS, imposed on baseline and mitigation scenarios for SSP3 and SSP5 that are consistent with the RCPs. We find that the reduced level of warming in RCP4.5 (relative to RCP8.5) can have either positive or negative effects on the economy since crop yield either increases or decreases with climate change depending on assumptions about CO2 fertilization. Yields are up to 12 % lower, and crop prices are up to 15 % higher, in RCP4.5 relative to RCP8.5 if CO2 fertilization is included, whereas yields are up to 22 % higher, and crop prices up to 22 % lower, if it is not. We also find that in the mitigation scenarios (RCP4.5), crop prices are substantially affected by mitigation actions as well as by climate impacts. For the scenarios we evaluated, the development pathway (SSP3 vs SSP5) has a larger impact on outcomes than climate (RCP4.5 vs RCP8.5), by a factor of 3 for crop prices, 11 for total cropland use, and 35 for GDP on global average. [ABSTRACT FROM AUTHOR]

This study examines how characteristics of tropical cyclones (TCs) that are explicitly resolved in a global atmospheric model with horizontal resolution of approximately 28 km are projected to change in a warmer climate using bias-corrected sea-surface temperatures (SSTs). The impact of mitigating from RCP8.5 to RCP4.5 is explicitly considered and is compared with uncertainties arising from SST projections. We find a reduction in overall global TC activity as climate warms. This reduction is somewhat less pronounced under RCP4.5 than under RCP8.5. By contrast, the frequency of very intense TCs is projected to increase dramatically in a warmer climate, with most of the increase concentrated in the NW Pacific basin. Extremes of storm related precipitation are also projected to become more common. Reduction in the frequency of extreme precipitation events is possible through mitigation from RCP8.5 to RCP4.5. In general more detailed basin-scale projections of future TC activity are subject to large uncertainties due to uncertainties in future SSTs. In most cases these uncertainties are larger than the effects of mitigating from RCP8.5 to RCP4.5. [ABSTRACT FROM AUTHOR]

Heavy precipitation exerts strong societal and economic impacts, including flooding, and these precipitation events are projected to increase under anthropogenic warming. The United Nations Framework Convention on Climate Change (UNFCCC) Paris Agreement signed in December 2015 aims to limit the global average temperature rise to below 2 °C above preindustrial levels, with an added goal of limiting temperature increases to 1.5 °C. There remains a major knowledge gap related to our understanding of changes in heavy precipitation under the 1.5 and 2 °C warming targets. Here, we investigate the changes in heavy precipitation events with the Community Earth System Model (CESM) climate experiments using the scenarios consistent with the 1.5 and 2 °C temperature targets. We find that the frequency of annual heavy precipitation at a global scale increases in both 1.5 and 2 °C scenarios until around 2070, after which the magnitudes of the trend become much weaker or even negative. Overall, the annual frequency of heavy precipitation across the globe is similar between 1.5 and 2 °C for the period 2006-2035, and the changes in heavy precipitation in individual seasons are consistent with those for the entire year. The frequency of heavy precipitation in the 2 °C experiments is higher than for the 1.5 °C experiment after the late 2030s, particularly for the period 2071-2100. While the results of both experiments indicate that the warming targets in the Paris Agreement, if met, would be effective in reducing the frequency of heavy precipitation (2 °C target minus 1.5 °C target), they also suggest a lower risk of global heavy precipitation under the 1.5 °C target of about 33% for the period 2071-2100. [ABSTRACT FROM AUTHOR]

In this study, the contributions from changes in man-made greenhouse gases (GHG), anthropogenic aerosols (AA), and land use (LU), as well as natural solar and volcanic (NAT) forcing changes, to observed changes in surface air temperature (T) and precipitation (P) over global land, especially over arid-semiarid areas, during 1946-2005 are quantified using observations and climate model simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Results show that the anthropogenic (ANT) forcings dominate the ubiquitous surface warming seen in observations and lead to slight increases in precipitation over most land areas, while the NAT forcing leads to small cooling over land. GHG increases are the primary factor responsible for the anthropogenic climate change, while the AA forcing offsets a large part of the GHG-induced warming and P changes. The LU forcing generally contributes little to the T and P changes from 1946 to 2005 over most land areas. Unlike the consistent temperature changes among most model simulations, precipitation changes display a large spread among the models and are incomparable with the observations in spatial distributions and magnitude, mainly due to its large internal variability that varies among individual model runs. Using an optimal fingerprinting method, we find that the observed warming over land during 1946-2005 can be largely attributed to the ANT forcings, and the combination of the ANT and NAT forcings can explain about 85~95% of the observed warming trend over global land as well as over most arid-semiarid regions such as Northern China. However, the anthropogenic influences on precipitation over the past 60 years are generally undetectable over most land areas, including most arid-semiarid regions. This indicates that internal variability is still larger than the forced change for land precipitation. [ABSTRACT FROM AUTHOR]

We investigate simulated hydrological extremes (i.e., high and low flows) under the present and future climatic conditions for five river basins worldwide: the Ganges, Lena, Niger, Rhine, and Tagus. Future projections are based on five GCMs and four emission scenarios. We analyse results from the HYPE, mHM, SWIM, VIC and WaterGAP3 hydrological models calibrated and validated to simulate each river. The use of different impact models and future projections allows for an assessment of the uncertainty of future impacts. The analysis of extremes is conducted for four different time horizons: reference (1981-2010), early-century (2006-2035), mid-century (2036-2065) and end-century (2070-2099). In addition, Sen's non-parametric estimator of slope is used to calculate the magnitude of trend in extremes, whose statistical significance is assessed by the Mann-Kendall test. Overall, the impact of climate change is more severe at the end of the century and particularly in dry regions. High flows are generally sensitive to changes in precipitation, however sensitivity varies between the basins. Finally, results show that conclusions in climate change impact studies can be highly influenced by uncertainty both in the climate and impact models, whilst the sensitivity to climate modelling uncertainty becoming greater than hydrological model uncertainty in the dry regions. [ABSTRACT FROM AUTHOR]

Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor. [ABSTRACT FROM AUTHOR]

We present an assessment of the impact of future climate change on two key drivers of fire risk in Australia, fire weather and fuel load. Fire weather conditions are represented by the McArthur Forest Fire Danger Index (FFDI), calculated from a 12-member regional climate model ensemble. Fuel load is predicted from net primary production, simulated using a land surface model forced by the same regional climate model ensemble. Mean annual fine litter is projected to increase across all ensemble members, by 1.2 to 1.7 t ha in temperate areas, 0.3 to 0.5 t ha in grassland areas and 0.7 to 1.1 t ha in subtropical areas. Ensemble changes in annual cumulative FFDI vary widely, from 57 to 550 in temperate areas, −186 to 1372 in grassland areas and −231 to 907 in subtropical areas. These results suggest that uncertainty in FFDI projections will be underestimated if only a single driving model is used. The largest increases in fuel load and fire weather are projected to occur in spring. Deriving fuel load from a land surface model may be possible in other regions, when this information is not directly available from climate model outputs. [ABSTRACT FROM AUTHOR]

Climate change has been projected to significantly affect agricultural productivity and hence food availability in the coming decades. The uncertainty associated with projecting climate change impacts is a barrier to agricultural adaptation. Despite uncertainty quantification becoming more prominent in impact studies, the thorough quantification of more than one uncertainty source is not commonly exercised. This work focuses on Indian groundnut and uses the General Large Area Model for annual crops (GLAM) to investigate the response of groundnut under future climate scenarios, develop a genotypic adaptation strategy, and quantify the main uncertainty sources. Results suggest that despite large uncertainty in yield projections (to which crop- and climate-related sources contribute 46 and 54 %, respectively) no-regret strategies are possible for Indian groundnut. Benefits from genotypic adaptation were robust towards the choice of climate model, crop model parameters and bias-correction methods. Groundnut breeding for 2030 climates should be oriented toward increasing maximum photosynthetic rates, total assimilate partitioned to seeds, and, where enough soil moisture is available, also maximum transpiration rates. No benefit from enhanced heat stress tolerance was observed, though this trait may become important as warming intensifies. Managing yield variability remains a challenge for groundnut, suggesting that an integral approach to crop adaptation that includes year-to-year coping strategies as well as improvements in crop management is needed across all India. [ABSTRACT FROM AUTHOR]

We consider the problem of projecting future climate from ensembles of regional climate model (RCM) simulations using results from the North American Regional Climate Change Assessment Program (NARCCAP). To this end, we develop a hierarchical Bayesian space-time model that quantifies the discrepancies between different members of an ensemble of RCMs corresponding to present day conditions, and observational records. Discrepancies are then propagated into the future to obtain high resolution blended projections of 21st century climate. In addition to blended projections, the proposed method provides location-dependent comparisons between the different simulations by estimating the different modes of spatial variability, and using the climate model-specific coefficients of the spatial factors for comparisons. The approach has the flexibility to provide projections at customizable scales of potential interest to stakeholders while accounting for the uncertainties associated with projections at these scales based on a comprehensive statistical framework. We demonstrate the methodology with simulations from the Weather Research & Forecasting regional model (WRF) using three different boundary conditions. We use simulations for two time periods: current climate conditions, covering 1971 to 2000, and future climate conditions under the Special Report on Emissions Scenarios (SRES) A2 emissions scenario, covering 2041 to 2070. We investigate and project yearly mean summer and winter temperatures for a domain in the South West of the United States. [ABSTRACT FROM AUTHOR]

Coastal populations in the global tropics and sub-tropics are vulnerable to the devastating impacts of hurricane storm surge and this risk is only expected to rise under climate change. In this study, we address this issue for the U.S. Gulf and Florida coasts. Using the framework of Potential Intensity, observations and output from coupled climate models, we show that the future large-scale thermodynamic environment may become more favorable for hurricane intensification. Under the RCP 4.5 emissions scenario and for the peak hurricane season months of August-October, we show that the mean intensities of Atlantic hurricanes may increase by 1.8-4.2 % and their lifetime maximum intensities may increase by 2.7-5.3 % when comparing the last two decades of the 20th and 21st centuries. We then combine our estimates of hurricane intensity changes with projections of sea-level rise to understand their relative impacts on future storm surge using simulations with the National Weather Service's SLOSH (Sea, Lake, and Overland Surges from Hurricanes) model for five historical hurricanes that made landfall in the Gulf of Mexico and Florida. Considering uncertainty in hurricane intensity changes and sea-level rise, our results indicate a median increase in storm surge ranging between 25 and 47 %, with changes in hurricane intensity increasing future storm surge by about 10 % relative to the increase that may result from sea level rise alone, with highly non-linear response of population at risk. [ABSTRACT FROM AUTHOR]

Future trends in debris flow activity are constructed based on bias-corrected climate change projections using two meteorological proxies: daily precipitation and Convective Available Potential Energy (CAPE) combined with specific humidity for two Alpine areas. Along with a comparison between proxies, future number of days with debris flows are analyzed with respect to different regional and global climate models, Representative Concentration Pathways (RCPs), and area for quantile mapping. Two different base periods are also analyzed, as debris flows were observed on only 6 (17) days between 1950 and 1979, yet on 18 (49) days between 1980 and 2009 for Fella River, NE Italy (Barcelonnette, SE French Alps). For both areas, future climate projections vary between no change up to an increase of 6.0 % per decade in days with debris flow occurrences towards the end of 21st century. In Barcelonnette, the base period and proxy have a bigger impact on the future number of debris flow days than the climate model or RCP used. In Fella River, the base period, RCP, and proxy used define the future range. Therefore the selection of proxy, base period and downscaling technique should be carefully considered for future climate change impact studies concerning debris flow activity and associated fast-moving landslides. [ABSTRACT FROM AUTHOR]

The study focuses on the analysis of extreme precipitation events of the present and future climate over southern Africa. Parametric and non-parametric approaches are used to identify and analyse these extreme events in data from the Coordinated Regional Climate Downscaling Experiment (CORDEX) models. The performance of the global climate model (GCM) forced regional climate model (RCM) simulations shows that the models are able to capture the observed climatological spatial patterns of the extreme precipitation. It is also shown that the downscaling of the present climate are able to add value to the performance of GCMs over some areas depending on the metric used. The added value over GCMs justifies the additional computational effort of RCM simulation for the generation of relevant climate information for regional application. In the climate projections for the end of twenty-first Century (2069-2098) relative to the reference period (1976-2005), annual total precipitation is projected to decrease while the maximum number of consecutive dry days increases. Maximum 5-day precipitation amounts and 95th percentile of precipitation are also projected to increase significantly in the tropical and sub-tropical regions of southern Africa and decrease in the extra-tropical region. There are indications that rainfall intensity is likely to increase. This does not equate to an increase in total rainfall, but suggests that when it does rain, the intensity is likely to be greater. These changes are magnified under the RCP8.5 when compared with the RCP4.5 and are consistent with previous studies based on GCMs over the region. [ABSTRACT FROM AUTHOR]

Given the severe impacts of hot summers on human and natural systems, we attempt to quantify future changes in extreme hot summer frequency in China using the Coupled Model Intercomparison Project Phase 5 (CMIP5) projections. Unlike previous studies focusing on fixed future time slices, we investigate the changes as a function of global mean temperature (GMT) rise. Analyses show that extreme hot summers (June-July-August mean temperature higher than 90 % quantile of 1971-2000 climatology) are projected to occur at least 80 % of the time across China with a GMT rise of 2 °C. The fraction of land area with extreme hot summers becoming the norm (median of future summer temperatures exceed the extreme) will increase from ~15 % with 0.5 °C of GMT rise to ~97 % with 2.5 °C GMT rise, which is much greater than for the global land surface as a whole. A distinct spatial pattern of the GMT rise threshold over which the local extreme hot summer first becomes the norm is revealed. When averaged over the country, the GMT rise threshold is 0.96 °C. Earth system models exhibit comparable results to climate system models, but with a relatively larger spread. Further analysis shows that the concurrence of hot and dry summers will increase significantly with the spatial structure of responses depending on the definition of drying. The increase of concurrent hot and dry conditions will induce potential droughts which would be more severe than those induced by only precipitation deficits. [ABSTRACT FROM AUTHOR]

Empirical statistical downscaling (ESD) methods seek to refine global climate model (GCM) outputs via processes that glean information from a combination of observations and GCM simulations. They aim to create value-added climate projections by reducing biases and adding finer spatial detail. Analysis techniques, such as cross-validation, allow assessments of how well ESD methods meet these goals during observational periods. However, the extent to which an ESD method's skill might differ when applied to future climate projections cannot be assessed readily in the same manner. Here we present a 'perfect model' experimental design that quantifies aspects of ESD method performance for both historical and late 21st century time periods. The experimental design tests a key stationarity assumption inherent to ESD methods - namely, that ESD performance when applied to future projections is similar to that during the observational training period. Case study results employing a single ESD method (an Asynchronous Regional Regression Model variant) and climate variable (daily maximum temperature) demonstrate that violations of the stationarity assumption can vary geographically, seasonally, and with the amount of projected climate change. For the ESD method tested, the greatest challenges in downscaling daily maximum temperature projections are revealed to occur along coasts, in summer, and under conditions of greater projected warming. We conclude with a discussion of the potential use and expansion of the perfect model experimental design, both to inform the development of improved ESD methods and to provide guidance on the use of ESD products in climate impacts analyses and decision-support applications. [ABSTRACT FROM AUTHOR]

Climate change is expected to affect health through changes in exposure to weather disasters. Vulnerability to coastal flooding has decreased in recent decades but remains disproportionately high in low-income countries. We developed a new statistical model for estimating future storm surge-attributable mortality. The model accounts for sea-level rise and socioeconomic change, and allows for an initial increase in risk as low-income countries develop. We used observed disaster mortality data to fit the model, splitting the dataset to allow the use of a longer time-series of high intensity, high mortality but infrequent events. The model could not be validated due to a lack of data. However, model fit suggests it may make reasonable estimates of log mortality risk but that mortality estimates are unreliable. We made future projections with and without climate change (A1B) and sea-based adaptation, but given the lack of model validation we interpret the results qualitatively. In low-income countries, risk initially increases with development up to mid-century before decreasing. If implemented, sea-based adaptation reduces climate-associated mortality in some regions, but in others mortality remains high. These patterns reinforce the importance of implementing disaster risk reduction strategies now. Further, while average mortality changes discontinuously over time, vulnerability and risk are evolving conditions of everyday life shaped by socioeconomic processes. Given this, and the apparent importance of socioeconomic factors that condition risk in our projections, we suggest future models should focus on estimating risk rather than mortality. This would strengthen the knowledge base for averting future storm surge-attributable health impacts. [ABSTRACT FROM AUTHOR]

Quantitative participatory exploration of the many complex issues surrounding cross-sectoral climate change impacts, vulnerability and adaptation under uncertain futures is dependent on the provision, in some form, of scenarios and scenario outputs. However, the normal provision by the research community of pre-defined scenario outputs results in a lack of flexibility for stakeholders regarding choice of climate models, scenarios, scenario quantification and output indicators which in turn can lead to a lack of trust. This Special Issue describes the development and application of a web-based interactive simulation and display environment, called the CLIMSAVE Integrated Assessment (IA) Platform, which provides a holistic (cross-sectoral, climate and socio-economic change) modelling framework. The IA Platform guides the user through simulation of (1) potential impacts under scenarios of climate and/or socio-economic change, (2) identification of sectoral and multi-sectoral vulnerability 'hotspots' either before or after adaptation, (3) the potential for adaptation to reduce impacts within the capital constraints of the selected scenario(s), and (4) the cost-effectiveness of adaptation measures. The Special Issue explores how the IA Platform has been: (i) designed to provide a user-friendly, intuitive and facilitating, rather than predictive or prescriptive, environment for users; and (ii) utilised to quantitatively explore a diverse range of uncertain futures across Europe. [ABSTRACT FROM AUTHOR]

Five stakeholder-relevant indices of agro-meteorological change were analysed for the UK, over past (1961-1990) and future (2061-2090) periods. Accumulated Frosts, Dry Days, Growing Season Length, Plant Heat Stress and Start of Field Operations were calculated from the E-Obs (European Observational) and HadRM3 (Hadley Regional Climate Model) PPE (perturbed physics ensemble) data sets. Indices were compared directly and examined for current and future uncertainty. Biases are quantified in terms of ensemble member climate sensitivity and regional aggregation. Maps of spatial change then provide an appropriate metric for end-users both in terms of their requirements and statistical robustness. A future UK is described with fewer frosts, fewer years with a large number of frosts, an earlier start to field operations (e.g., tillage), fewer occurrences of sporadic rainfall, more instances of high temperatures (in both the mean and upper range), and a much longer growing season. [ABSTRACT FROM AUTHOR]

Climate change vulnerability depends on who you are, where you are and what you do. The indigenous communities who primarily depend on natural resources for subsistence livelihoods are among the first and most affected by climate change. Climate models have predicted pronounced warming in high altitude regions of the Himalayas. The transhumant communities of the Himalayas follow traditional lifestyles based on seasonal livestock rearing and subsistence agriculture. There is however, no information on how vulnerable transhumant communities are to climate change, and how vulnerability of transhumant herders differs across the mountainous areas of Nepal. Based on semi-structured interviews with transhumant herders and using the IPCC climate change vulnerability framework, this study assessed and compared the vulnerability of transhumant communities from three districts representing Eastern, Central and Western mountainous region of Nepal. The results showed that the livelihood vulnerability and the climate change vulnerability differ across sites; both of them having lowest index values in the Central region. The vulnerability dimensions viz. exposure, sensitivity and adaptive capacity are largely influenced by diversity in livelihood strategies, income sources and crops, and access to food, water and health facilities. The findings will inform the design of policies and programmes to reduce vulnerability and enhance adaptive capacity of indigenous communities in general and the transhumant communities of the Himalayas in particular. [ABSTRACT FROM AUTHOR]